Cumulative Plot Sales

Column

Plots Sold Across the World

Row

Plot Locations

Plots Sold in the US

World Plots

Column

Cumulative Plot Sales

country sold
United States 3211
United Kingdom 329
France 211
Australia 157
United Arab Emirates 117
Spain 96
Italy 95
Japan 93
Canada 80
China 79
Switzerland 78
Germany 77
Netherlands 69
Singapore 60
Egypt 44
Hong Kong 44
India 44
Russia 40
Brazil 38
Portugal 37
Mexico 32
Peru 32
Monaco 31
Uzbekistan 31
Vatican City 27
Greece 26
Israel 26
Cambodia 25
Poland 25
South Korea 20
Belgium 18
Turkey 18
Saudi Arabia 16
Panama 15
Austria 13
New Zealand 13
Thailand 12
Ukraine 12
South Africa 11
coordinates 7
Czech Republic 6
Indonesia 6
Ireland 6
Finland 5
Zimbabwe 5
Denmark 4
Georgia 4
Iraq 4
Jamaica 4
Jordan 4
Malaysia 4
Philippines 4
Venezuela 4
NA 4
Argentina 3
Bahamas 3
Iceland 3
Kazakhstan 3
Saint Barthelemy 3
Saint Lucia 3
Taiwan 3
Armenia 2
Azerbaijan 2
Colombia 2
Croatia 2
Cuba 2
French Polynesia 2
Guatemala 2
Nepal 2
Ontario 2
Qatar 2
Bolivia 1
Bulgaria 1
Chad 1
Chile 1
Costa Rica 1
Cyprus 1
Dominican Republic 1
Estonia 1
Jerusalem District 1
Luxembourg 1
Malta 1
Mongolia 1
Montenegro 1
North Korea 1
Norway 1
Palestinian Territories 1
Romania 1
Senegal 1
Serbia 1
Sri Lanka 1
Vietnam 1

Column

Day

Week

Month

Year

Total

US Plots

Column

Cumulative US Plot Sales

state sold
Texas 696
California 561
New York 542
Florida 311
Nevada 262
District of Columbia 75
Illinois 69
Massachusetts 56
Tennessee 56
Louisiana 53
Georgia 44
Pennsylvania 39
Washington 37
Minnesota 33
Kentucky 31
Ohio 30
Missouri 29
Hawaii 28
Indiana 23
Colorado 22
New Jersey 22
Arizona 21
Michigan 21
North Carolina 19
Alabama 16
Maryland 13
Oklahoma 13
Wyoming 13
Arkansas 12
Virginia 11
Wisconsin 11
Oregon 10
Utah 10
South Carolina 4
Maine 3
South Dakota 3
Connecticut 2
New Mexico 2
Delaware 1
Iowa 1
Kansas 1
Mississippi 1
Nebraska 1
New York L2GX5 1
North Dakota 1
Ontario 1

Column

Day

Week

Month

Year

Total

---
title: "SuperWorld Plot Sales"
output: 
  flexdashboard::flex_dashboard:
    social: menu
    source_code: embed
    theme: yeti
---

Cumulative Plot Sales
=====================================

Inputs {.sidebar}
-------------------------------------

```{r setup, include=FALSE, warning=FALSE, message=FALSE}
library(leaflet)
library(leaflet.extras)
library(sf)
library(tidyverse)
library(rnaturalearth)
library(rnaturalearthdata)
library(plotly)
library(usmap)
library(lubridate)

plots_sold = read_csv("C:/Users/rebec/SuperWorld_Plot_Recommendation/data/plots_sold.csv")[-1]
plots_sold$code = toupper(plots_sold$code)

us_plots = plots_sold[which(plots_sold$code == "US"),]
us_address = us_plots$address

state = c()
for (i in 1:length(us_address)){
  add = tail(unlist(str_split(us_address[i], pattern = ", ")), 2)[1]
  add = gsub(' [[:digit:]]+', '', add)
  state = c(state, add)
}

us_plots = cbind(us_plots, state) 

state_data = data.frame(state) %>%
  group_by(state) %>%
  summarise(sold = n())

```

*Total Plot Sales:*

```{r}
nrow(plots_sold)
```


*Top 10 Countries:* ```{r} plots_sold %>% group_by(country) %>% summarise(`plots sold` = n()) %>% arrange(-`plots sold`) %>% head(10) %>% knitr::kable() ```
*Top 10 US States:* ```{r} state_data %>% summarise(state, `plots sold` = sold) %>% arrange(-`plots sold`) %>% head(10) %>% knitr::kable() ``` Column {data-width=800} ------------------------------------- ### Plots Sold Across the World ```{r warning=FALSE, message=FALSE} world = ne_countries(scale = "medium", returnclass = "sf") df = st_sf(merge(plots_sold, world, by.x = "code", by.y = "iso_a2", all.x = FALSE, all.y = TRUE, returnclass = "sf")) df_plot = df %>% group_by(country, code) %>% summarise(sold = n()) %>% mutate(sold = ifelse(is.na(country), 0, sold)) %>% ggplot() + geom_sf(aes(fill = sold))+ scale_fill_gradient(trans = "log") + geom_sf_text(aes(label = code), size = 1) + theme(axis.title.x=element_blank(), axis.title.y=element_blank(), legend.title = element_text("Plots Sold")) + labs(caption = "Sold values are in log scale") + guides(fill = guide_colourbar(barwidth = 0.5, barheight = 10)) # df2 = df %>% # group_by(country, code) %>% # summarise(sold = n()) %>% # mutate(sold = ifelse(is.na(country), 0, sold)) # plot(df2["sold"], logz = TRUE, main = NULL, key.pos = 4) ggplotly(df_plot) %>% layout(annotations = list(x = 1, y = -0.1, text = "Sold values are in log scale", showarrow = F, xref='paper', yref='paper', xanchor='right', yanchor='auto', xshift=-10, yshift=80, font=list(size=15)), xaxis = list(autorange = TRUE), yaxis = list(autorange = TRUE) ) ``` Row ------------------------------------- ### Plot Locations ```{r} leaflet(plots_sold) %>% addTiles() %>% addCircles(lng = ~lon, lat = ~lat) %>% setView(lat = 37.0902, lng = -95.7129, zoom = 4) ``` ### Plots Sold in the US ```{r} us = plot_usmap(data = state_data, values = "sold", regions = "states") + theme(legend.position = "right") + scale_fill_continuous(name = "Plots Sold") ggplotly(us) ``` World Plots ===================================== Column {data-width=200} ------------------------------------- ### Cumulative Plot Sales ```{r} plots_sold %>% group_by(country) %>% summarize(sold = n()) %>% arrange(-sold) %>% knitr::kable() ``` Column {.tabset} ------------------------------------- ### Day ```{r} plots_today = plots_sold %>% mutate(days = interval(date, today()) %/% days(1)) %>% filter(days < 1) df_today = st_sf(merge(plots_today, world, by.x = "code", by.y = "iso_a2", all.x = FALSE, all.y = TRUE, returnclass = "sf")) plot = df_today %>% group_by(country, code) %>% summarise(sold = n()) %>% mutate(sold = ifelse(is.na(country), 0, sold)) %>% ggplot() + geom_sf(aes(fill = sold))+ scale_fill_gradientn(colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) + geom_sf_text(aes(label = code), size = 1) + theme(axis.title.x=element_blank(), axis.title.y=element_blank(), legend.title = element_text("Plots Sold")) + labs(caption = "Sold values are in log scale") + guides(fill = guide_colourbar(barwidth = 0.5, barheight = 10)) ggplotly(plot) %>% layout(xaxis = list(autorange = TRUE), yaxis = list(autorange = TRUE)) ``` ### Week ```{r} plots_week = plots_sold %>% mutate(days = interval(date, today()) %/% days(1)) %>% filter(days < 7) df_week = st_sf(merge(plots_week, world, by.x = "code", by.y = "iso_a2", all.x = FALSE, all.y = TRUE, returnclass = "sf")) plot = df_week %>% group_by(country, code) %>% summarise(sold = n()) %>% mutate(sold = ifelse(is.na(country), -Inf, sold)) %>% ggplot() + geom_sf(aes(fill = sold))+ scale_fill_gradientn(trans = "log", colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) + geom_sf_text(aes(label = code), size = 1) + theme(axis.title.x=element_blank(), axis.title.y=element_blank(), legend.title = element_text("Plots Sold")) + labs(caption = "Sold values are in log scale") + guides(fill = guide_colourbar(barwidth = 0.5, barheight = 10)) ggplotly(plot) %>% layout(annotations = list(x = 1, y = -0.1, text = "Sold values are in log scale", showarrow = F, xref='paper', yref='paper', xanchor='right', yanchor='auto', xshift=-10, yshift=80, font=list(size=15)), xaxis = list(autorange = TRUE), yaxis = list(autorange = TRUE) ) ``` ### Month ```{r} plots_month = plots_sold %>% mutate(days = interval(date, today()) %/% days(1)) %>% filter(days < 30) df_month = st_sf(merge(plots_month, world, by.x = "code", by.y = "iso_a2", all.x = FALSE, all.y = TRUE, returnclass = "sf")) plot = df_month %>% group_by(country, code) %>% summarise(sold = n()) %>% mutate(sold = ifelse(is.na(country), -Inf, sold)) %>% ggplot() + geom_sf(aes(fill = sold))+ scale_fill_gradientn(trans = "log", colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) + geom_sf_text(aes(label = code), size = 1) + theme(axis.title.x=element_blank(), axis.title.y=element_blank(), legend.title = element_text("Plots Sold")) + labs(caption = "Sold values are in log scale") + guides(fill = guide_colourbar(barwidth = 0.5, barheight = 10)) ggplotly(plot) %>% layout(annotations = list(x = 1, y = -0.1, text = "Sold values are in log scale", showarrow = F, xref='paper', yref='paper', xanchor='right', yanchor='auto', xshift=-10, yshift=80, font=list(size=15)), xaxis = list(autorange = TRUE), yaxis = list(autorange = TRUE) ) ``` ### Year ```{r} plots_year = plots_sold %>% mutate(days = interval(date, today()) %/% days(1)) %>% filter(days < 365) df_year = st_sf(merge(plots_year, world, by.x = "code", by.y = "iso_a2", all.x = FALSE, all.y = TRUE, returnclass = "sf")) plot = df_year %>% group_by(country, code) %>% summarise(sold = n()) %>% mutate(sold = ifelse(is.na(country), -Inf, sold)) %>% ggplot() + geom_sf(aes(fill = sold))+ scale_fill_gradientn(trans = "log", colors = colorspace::heat_hcl(5, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) + geom_sf_text(aes(label = code), size = 1) + theme(axis.title.x=element_blank(), axis.title.y=element_blank(), legend.title = element_text("Plots Sold")) + labs(caption = "Sold values are in log scale") + guides(fill = guide_colourbar(barwidth = 0.5, barheight = 10)) ggplotly(plot) %>% layout(annotations = list(x = 1, y = -0.1, text = "Sold values are in log scale", showarrow = F, xref='paper', yref='paper', xanchor='right', yanchor='auto', xshift=-10, yshift=80, font=list(size=15)), xaxis = list(autorange = TRUE), yaxis = list(autorange = TRUE) ) ``` ### Total ```{r} plot = df %>% group_by(country, code) %>% summarise(sold = n()) %>% mutate(sold = ifelse(is.na(country), -Inf, sold)) %>% ggplot() + geom_sf(aes(fill = sold))+ scale_fill_gradientn(trans = "log", colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) + geom_sf_text(aes(label = code), size = 1) + theme(axis.title.x=element_blank(), axis.title.y=element_blank(), legend.title = element_text("Plots Sold")) + labs(caption = "Sold values are in log scale") + guides(fill = guide_colourbar(barwidth = 0.5, barheight = 10)) ggplotly(plot) %>% layout(annotations = list(x = 1, y = -0.1, text = "Sold values are in log scale", showarrow = F, xref='paper', yref='paper', xanchor='right', yanchor='auto', xshift=-10, yshift=80, font=list(size=15)), xaxis = list(autorange = TRUE), yaxis = list(autorange = TRUE) ) ``` US Plots ===================================== Column {data-width=200} ------------------------------------- ### Cumulative US Plot Sales ```{r} us_plots %>% group_by(state) %>% summarize(sold = n()) %>% arrange(-sold) %>% knitr::kable() ``` Column {.tabset} ------------------------------------- ### Day ```{r} us_today = us_plots %>% mutate(days = interval(date, today()) %/% days(1)) %>% filter(days < 1) %>% group_by(state) %>% summarise(sold = n()) us_today = plot_usmap(data = us_today, values = "sold", regions = "states") + theme(legend.position = "right") + # scale_fill_continuous(name = "Plots Sold") + scale_fill_gradientn(name = "Plots Sold", colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) ggplotly(us_today) ``` ### Week ```{r} us_week = us_plots %>% mutate(days = interval(date, today()) %/% days(1)) %>% filter(days < 7) %>% group_by(state) %>% summarise(sold = n()) us_week = plot_usmap(data = us_week, values = "sold", regions = "states") + theme(legend.position = "right") + scale_fill_gradientn(name = "Plots Sold", colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) ggplotly(us_week) ``` ### Month ```{r} us_month = us_plots%>% mutate(days = interval(date, today()) %/% days(1)) %>% filter(days < 30) %>% group_by(state) %>% summarise(sold = n()) us_month = plot_usmap(data = us_month, values = "sold", regions = "states") + theme(legend.position = "right") + scale_fill_gradientn(name = "Plots Sold", colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) ggplotly(us_month) ``` ### Year ```{r} us_year = us_plots%>% mutate(days = interval(date, today()) %/% days(1)) %>% filter(days < 365) %>% group_by(state) %>% summarise(sold = n()) us_year = plot_usmap(data = us_year, values = "sold", regions = "states") + theme(legend.position = "right") + scale_fill_gradientn(name = "Plots Sold", colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) ggplotly(us_year) ``` ### Total ```{r} us = plot_usmap(data = state_data, values = "sold", regions = "states") + theme(legend.position = "right") + scale_fill_gradientn(name = "Plots Sold", colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) ggplotly(us) ```